tiny-gpt2-magicprompt
Generate/augment your prompt, stable diffusion style. Enter a new dimension of creativity
This model is a fine-tuned version of sshleifer/tiny-gpt2 on the Gustavosta/Stable-Diffusion-Prompts dataset. It achieves the following results on the evaluation set:
- Loss: 10.7918
- perplexity: 48618.8756
Intended uses & limitations
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Training and evaluation data
refer to the Gustavosta/Stable-Diffusion-Prompts dataset.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 10.8201 | 0.96 | 16 | 10.8191 |
| 10.8167 | 1.96 | 32 | 10.8145 |
| 10.8117 | 2.96 | 48 | 10.8095 |
| 10.8058 | 3.96 | 64 | 10.8025 |
| 10.7997 | 4.96 | 80 | 10.7989 |
| 10.7959 | 5.96 | 96 | 10.7947 |
| 10.7934 | 6.96 | 112 | 10.7925 |
| 10.7924 | 7.96 | 128 | 10.7919 |
| 10.7921 | 8.96 | 144 | 10.7918 |
| 10.792 | 9.96 | 160 | 10.7918 |
Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1
